Plot a single cell copy number profile

plotCNprofile(
  CNbins,
  cellid = NULL,
  chrfilt = NULL,
  pointsize = 1,
  alphaval = 0.6,
  maxCN = 10,
  cellidx = 1,
  statecol = "state",
  returnlist = FALSE,
  raster = FALSE,
  genome = "hg19",
  y_axis_trans = "identity",
  xaxis_order = "genome_position",
  legend.position = "bottom",
  annotateregions = NULL,
  annotateregions_linetype = 2,
  SV = NULL,
  SVcol = TRUE,
  svalpha = 0.5,
  svwidth = 1,
  adj = 0.03,
  genes = NULL,
  tickwidth = 50,
  chrstart = NULL,
  chrend = NULL,
  shape = 16,
  positionticks = FALSE,
  ideogram = FALSE,
  overwrite_color = NULL,
  sv_style = "curves",
  sv_arc_alpha = 0.3,
  sv_arc_height = 0.2,
  show_sv_read_axis = TRUE,
  sv_read_axis_scale = NULL,
  show_sv_legend = FALSE,
  ...
)

Arguments

CNbins: Single cell copy number dataframe with the following columns: cell_id, chr, start, end, state, copy
cellid: Which cell to plot, if no cell is specific will plot the first cell in the dataframe
chrfilt: Vector of chromosomes to plot, if NULL (default) will plot all chromosomes
pointsize: The point size in the plot
alphaval: Alpha value of points
maxCN: The maximum on the y axis, if any points are above this value they will be winsorized rather than removed
cellidx: idx of cell to plot if cellid = NULL
statecol: The colour mapping, default is to map colours to the state column
returnlist: Return a list rather than the ggplot object
raster: use ggrastr or not, default = FALSE
genome: genome to use, default = "hg19" (only used for ideogram)
y_axis_trans: What transformation to use on the y-axis, default is identity, the other option is "squashy" which uses a tanh transformation
xaxis_order: Default is "genome_position"
legend.position: Where to place the legend, default is "bottom"
annotateregions: Dataframe with chr start and end positions to annotate, will draw a dashed vertical line at this position
annotateregions_linetype: linetype for region annotation, default = 2 (dashed)
SV: Default is NULL. If a dataframe with structural variant position is passed it will add rearrangement links between bins. For lines_and_arcs style, SV must include columns: strand_1, strand_2 (values '+' or '-'), and read_count.
SVcol: Default is TRUE. Colour SVs or not
svalpha: the alpha scaling of the SV lines, default = 0.5
svwidth: Width of SV width curves, default = 1.0
adj: adjustment for gene labels
genes: vector of genes to annotate, will add a dashed vertical line and label
tickwidth: Spacing of ticks (in Mb) when only 1 chromosome is plotted
chrstart: Start of region (in Mb) when plotting a single chromosome
chrend: End of region (in Mb) when plotting a single chromosome
shape: shape for plotting, default = 16
positionticks: set to TRUE to use position ticks rather than chromosome ticks
ideogram: plot ideogram at the top, default = TRUE
sv_style: visualization style for structural variants: "curves" (default, bezier curves), "lines_and_arcs" (colored lines with arcs), or "both"
sv_arc_alpha: transparency of SV arcs when using lines_and_arcs style, default = 0.3
sv_arc_height: height of SV arcs as fraction of maxCN when using lines_and_arcs style, default = 0.2
show_sv_read_axis: show secondary y-axis for SV read support when using lines_and_arcs style, default = TRUE. Works with both identity and squashy y-axis transformations.
sv_read_axis_scale: maximum value for SV read support axis (auto-scaled if NULL), default = NULL
show_sv_legend: show legend for SV orientations when using lines_and_arcs style, default = FALSE

Value

ggplot2 plot

Examples


plotCNprofile(CNbins)
#> Making CN profile and BAF plot for cell - SA921-A90554A-R03-C44
#> Making fixed-width bins for bin size 500000 ...